Glossary

Artificial General Intelligence (AGI)

What is Artificial General Intelligence (AGI)?

Artificial general intelligence (AGI) refers to a hypothetical AI system that can handle a broad range of intellectual tasks at human level and flexibly transfer what it has learned to new areas. Today's systems perform well on many tasks but still show limitations, for instance in reliability. There is no generally accepted definition or way of measuring it, and whether and when AGI will be achieved is disputed among experts.

Topics

  • AI fundamentals
  • Research
  • Concept

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Related terms

Artificial Superintelligence (ASI)

Artificial superintelligence (ASI) is a theoretical concept for an AI that significantly surpasses humans in almost all intellectual abilities, including research, planning and social judgement. No such system exists. The term is used mainly in philosophy and AI safety research to discuss questions of control and of aligning future systems with human values.

  • AI fundamentals
  • AI safety
  • +1

Singularity

The technological singularity is a theoretical notion: a point at which artificial intelligence improves itself so quickly that technological progress can no longer be predicted or controlled by humans. The term was popularised by the author Vernor Vinge and the futurist Ray Kurzweil. Whether and when this might happen is scientifically open and disputed. Nevertheless, the idea influences discussions about AI safety, regulation and long-term risks.

  • future scenario
  • superintelligence
  • +1

Turing Test

The Turing test goes back to the mathematician Alan Turing, who described it in 1950 as the “imitation game”. A person holds written conversations with a human and a machine; if they cannot reliably tell which is the machine, the test is considered passed. Today's language models often hold very human-sounding conversations, but the test only says something about convincing language, not about understanding or reliability.

  • History
  • AI basics
  • +1

Foundation model

A foundation model is a large AI model pre-trained on very extensive, wide-ranging data that can then be used for many different tasks. The term was coined at Stanford University in 2021. Well-known examples include large language models such as Claude or GPT, as well as image models. Businesses usually build their applications on such models rather than training their own from scratch; the EU AI Act refers to general-purpose AI models.

  • Base model
  • Language models
  • +1